Beyaztas, Ufuk; Alin, Aylin; Martin, Michael
The Jackknife-after-bootstrap (JaB) technique originally developed by Efron  has been proposed as an approach to improve the detection of influential observations in linear regression models by Martin and Roberts  and Beyaztas and Alin . The method is based on the use of percentile-method confidence intervals to provide improved cut-off values for several single case-deletion influence measures. In order to improve JaB, we propose using robust versions of Efron 's bias-corrected...[Show more]
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